Projecting television parameters onto 2D space for daypart competitor analysis

ABSTRACT

A system and method for determining a similarity measure between a first network broadcast time segment and a second network broadcast time segment. The method includes generating first training data comprising groupings of similar broadcast networks and groupings of dissimilar broadcast networks, extracting second training data comprising audience data and genre data for time segments of network broadcasts, and training an encoder neural network based on the first and second training data. The method includes executing the trained neural network with input data comprising the first network broadcast time segment and determining a similarity measure relative to the second network broadcast time segment.

PRIORITY DATA

The present application is a Continuation application of U.S. patentapplication Ser. No. 16/745,092 filed on Jan. 16, 2020, now U.S. Pat.No. 11,375,279; the entire disclosure of the aboveapplication(s)/patent(s) is expressly incorporated herein by reference.

BACKGROUND

The dayparting process divides the television broadcast day into severalsegments, where content is aired based on the viewing habits of thetarget audience for that segment. For example, morning shows generallyair between e.g. 7 am and 10 am, while popular scripted programsgenerally air between e.g. 7 pm and 10 pm (primetime). Networks have adegree of similarity with respect to the type of content aired over thecourse of the day. For example, Nickelodeon® and Cartoon Network® mayair content with a high degree of similarity while MTV® and HistoryChannel® may air content with a low degree of similarity. Determiningthe degree of similarity between network dayparts may provide valuableinformation to the networks themselves, ad sellers, or other entities.

Traditional approaches for determining content similarity includeaudience demographic and genre duration differencing or proportioningand the use of vector cosine similarity. The existing differencingmethods are highly subjective and therefore depend on the judgment ofthe analyst. Cosine similarity is a more objective method but comparesonly two entries at a time.

SUMMARY

The present disclosure is directed to a method comprising generatingfirst training data comprising groupings of similar broadcast networksand groupings of dissimilar broadcast networks and extracting secondtraining data comprising audience data and genre data for time segmentsof network broadcasts. The method further comprises training an encoderneural network based on the first and second training data and executingthe trained neural network with input data comprising at least a firstnetwork broadcast time segment. The method further comprises determininga similarity measure between the first network broadcast time segmentand a second network broadcast time segment.

The present disclosure is further directed to a system comprising amemory storing first training data comprising groupings of similarbroadcast networks and groupings of dissimilar broadcast networks andsecond training data comprising audience data and genre data for timesegments of network broadcasts. The system further comprises a processortraining an encoder neural network based on the first and secondtraining data, executing the trained neural network with input datacomprising at least a first network broadcast time segment, anddetermining a similarity measure between the first network broadcasttime segment and a second network broadcast time segment.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a system for content comparison and similaritydetermination according to various exemplary embodiments of the presentdisclosure.

FIG. 2 shows a method for training an encoder network for generating acontent similarity comparison for network dayparts according to variousexemplary embodiments of the present disclosure.

FIG. 3 shows an exemplary Siamese neural network diagram.

FIG. 4 shows a method for generating a content similarity comparison fornetwork dayparts and plotting the output according to various exemplaryembodiments of the present disclosure.

FIG. 5 shows an exemplary scatterplot output for the method of FIG. 4 .

DETAILED DESCRIPTION

The exemplary embodiments may be further understood with reference tothe following description and the related appended drawings, whereinlike elements are provided with the same reference numerals. Theexemplary embodiments relate to a system and method for determining asimilarity of content and viewership data and visualizing the degree ofsimilarity. The exemplary embodiments include a dimensionality reductionfor high dimensional input data, particularly an audience demographicdistribution and a genre hour distribution, into a 2-dimensional spacewhere broadcasting segments, e.g. dayparts, are compared for similarity.The output of the exemplary method may be a 2D plot where similarsegments are mapped to the plot at short Euclidean distances from eachother and dissimilar dayparts are mapped to the plot at long Euclideandistances from each other. Notably, any number of networks and theirrespective dayparts can be compared using this method, with the outputplot providing an easy interpretation for multiple entries. Although theexemplary embodiments will be described with respect to televisionbroadcast comparisons, other media content such as radio and digital maybe compared in a similar manner.

FIG. 1 shows a system 100 for content comparison and similaritydetermination according to various exemplary embodiments of the presentdisclosure. The system 100 includes a computing device 105 comprising aprocessor 110 for executing a neural network and projecting daypartaudience and genre data onto a two-dimensional plot where similarsegments are mapped to the plot at short Euclidean distances from eachother and dissimilar dayparts are mapped to the plot at long Euclideandistances from each other. The system 100 includes a display 115 forpresenting the plot to a user. The system 100 includes a memory 120 forstoring broadcast data. The memory 120 may comprise a database ofnetwork audience and genre distribution data for all networks andprograms broadcast thereon. For example, the database may compriseNielsen® data. The database may include multiple databases. The memory120 may store the e.g. Nielsen data directly, or the device 105 mayaccess the data from a remote storage. The system includes a userinterface 125 for e.g. inputting the network dayparts to be run throughthe neural network and compared for similarity.

FIG. 2 shows a method 200 for training an encoder network for generatinga content similarity comparison for network dayparts according tovarious exemplary embodiments of the present disclosure.

In 205, groups of similar networks and groups of dissimilar networks aregenerated as training data. The groupings may be manually generated. Forexample, similar networks such as CNN® and MSNBC®, Nickelodeon® andCartoon Network®, etc., may be grouped based on their traditionalrecognition of similarity, and networks such as CNN® and Golf Channel®,MTV® and History Channel®, etc., may be grouped together based on theirtraditional recognition of dissimilarity. In another embodiment, thegroupings are generated based on a comparison of network program genresover time segments. For example, two networks are paired as similar whengreater than a predetermined percentage of their programming genresoverlap, or as dissimilar when fewer than a predetermined percentage oftheir programming genres overlap. A combination of human judgment anddata analysis may be used to generate the similar/dissimilar pairs. Inthe exemplary embodiment described herein, 460 pairs are generated totrain the model. However, greater or fewer than 460 pairs may be used.

In 210, network audience and genre data is extracted as distributiondata for all programs and all networks as additional training data andcharacterized using similarity descriptors. The data, including e.g.,program genre, audience age, audience demographics, audience location,etc., may be stored on and extracted from the memory 120 and may be e.g.Nielsen® data, data from another programming measurement entity, orinternal programming measurement data. The data is extracted asn-dimensional vectors comprising a number of similarity descriptors,e.g., 62 descriptors. The similarity descriptors are determined bycomputing feature contribution weights (coefficients) in a logisticregression designed to detect similar networks regardless of daypart.Features with high magnitude weights (positive or negative) hadconsiderable contribution towards similarity status. The similaritydescriptors are more indicative of the fine-grained characteristics ofthe programming than any other set of descriptors.

In 215, the training data from steps 205 and 210 is fit to a neuralnetwork to train the neural network. The neural network may be, forexample, a Siamese neural network built with an encoder network thatreduces the number of data dimensions to two, where the definingcharacteristics responsible for differentiating networks is found.

As would be known to a person skilled in the art, an encoder/decoderarchitecture first maps an input data set to a latent space (forwardencoding) and subsequently maps the output of the first mapping back tothe original space (backward decoding). A contrastive loss functionlearns the parameters W of a parameterized function G_(W) in such a waythat neighbors are pulled together and non-neighbors are pushed apart.For a family of functions G, parameterized by W, a value of W is foundthat maps the set of high dimensional inputs to the manifold such thatthe Euclidean distance between points on the manifold approximates thesimilarity of the inputs in input space. FIG. 3 shows a Siamese neuralnetwork diagram, where X₁ and X₂ are input vector pairs, Y is the outputindicator variable signifying similar or dissimilar input pairs, EN₁ andEN₂ are encoder networks with the same weights W, i.e., they are thesame network. ∥-∥₂ represents contrastive loss.

In 220, the trained encoder network is isolated. Those skilled in theart understand that an encoder neural network model is nested inside anouter neural network model. The coefficients in both models aredetermined by minimizing the prediction error of the outer model. Oncetraining is completed, the outer model is no longer needed and the innerencoder network is written to disk. The trained encoder network is nowready to be used for a network/daypart similarity comparison.

An exemplary implementation of the encoder network in python programminglanguage using the keras library is shown below:

-   -   input=Input(shape=input_shape)    -   x=Dense(128, activation=‘relu’)(input)    -   x=Dropout(0.2)(x)    -   x=Dense(2, activation=‘relu’)(x)    -   model=Model(inputs=[input], outputs=[x])

The method 200 is executed only once to train the encoder network.

FIG. 4 shows a method 400 for generating a content similarity comparisonfor network dayparts and plotting the output according to variousexemplary embodiments of the present disclosure.

In 405, a plurality of network dayparts to be compared are input to thecomputing device 105 via e.g. the user interface 125. The programmingand audience data for the dayparts (converted to the similarity featuresdiscussed above) are used as the model input.

In 410, the trained encoder network generated in the method 300 isexecuted with the genre hours and audience distribution data for thenetworks input in step 405. As discussed previously, the trained encodernetwork pulls similar network dayparts closer together and pushesdissimilar network dayparts further apart.

In 415, the output of the execution of the encoder network is graphed ona two-dimensional plot. The networks input in step 405 are plotted inthe graphical display so that similar networks are short Euclideandistances away from each other and dissimilar networks are longEuclidean distances away from each other. FIG. 5 shows an exemplaryscatterplot output for the method of FIG. 4 , where four networks(Cartoon Network®, CNN®, MSNBC® and Nickelodeon®) are compared forsimilarity. However, any number of networks may be compared.

The above-described embodiment is directed to comparing a same daypartacross different networks. However, the exemplary embodiments are notlimited to this specific implementation. For example, the compareddayparts need not overlap in time. A first daypart for a network may becompared to a second, non-overlapping daypart for the same network, or afirst daypart for a first network may be compared to a second,non-overlapping daypart for a second network. In another embodiment, asingle network daypart may be input to the model and one or moremost-similar network dayparts may be generated and presented to theuser. The trained encoder network may be used for comparing networkcontent in additional ways, as would be known to a person skilled in theart.

It will be apparent to those skilled in the art that variousmodifications may be made to the present disclosure, without departingfrom the spirit or the scope of the exemplary embodiments. Thus, it isintended that the present disclosure cover modifications and variationsof the exemplary embodiments provided they come within the scope of theappended claims and their equivalent.

The invention claimed is:
 1. A method, comprising: generating firsttraining data comprising pairs of broadcast network identitiesdetermined to be similar to one another and pairs of broadcast networkidentities determined to be dissimilar to one another, wherein thebroadcast network identities are selected from a predetermined set ofbroadcast networks; extracting second training data comprising audiencedata and genre data for each broadcast network of the predetermined setof broadcast networks; training an encoder neural network based on thefirst and second training data; executing the trained neural networkwith input data for a broadcast during a first time segment on a firstnetwork from the predetermined set of broadcast networks; anddetermining a similarity measure between the first time segment on thefirst network and a second time segment on a second network from thepredetermined set of broadcast networks; and outputting the similaritymeasure between the first time segment on the first network and thesecond time segment on the second network.
 2. The method of claim 1,wherein the trained neural network is executed with input datacomprising the first time segment of the first network and the secondtime segment of the second network and at least one additional thirdtime segment of a third network from the predetermined set of broadcastnetworks, the trained neural network determining similarity measuresbetween the first, second and third time segments.
 3. The method ofclaim 2, further comprising: graphing the time segments on atwo-dimensional plot where time segments of the pairs of broadcastnetwork identities determined to be similar to one another are mapped tothe plot at short distances from each other and time segments of thepairs of broadcast network identities determined to be dissimilar to oneanother are mapped to the plot at long distances from each other.
 4. Themethod of claim 1, further comprising: determining a time segment of anetwork from the predetermined set of broadcast networks having ahighest degree of similarity to the first time segment of the firstnetwork.
 5. The method of claim 1, wherein the audience data includesaudience demographic distribution data and the genre data includes genredistribution data.
 6. The method of claim 5, wherein the second trainingdata is extracted as n-dimensional vectors of a predetermined number ofsimilarity descriptors for each broadcast network of the predeterminedset of broadcast networks.
 7. The method of claim 6, wherein eachbroadcast network of the predetermined set of broadcast networks ischaracterized with 62 similarity descriptors.
 8. The method of claim 1,wherein the first training data is generated manually or is generatedbased on a comparison of network program genres.
 9. The method of claim1, wherein the encoder neural network is a Siamese neural network.
 10. Asystem, comprising: a memory storing first training data comprisingpairs of broadcast network identities determined to be similar to oneanother and pairs of broadcast network identities determined to bedissimilar to one another, wherein the broadcast network identities areselected from a predetermined set of broadcast networks, and storingsecond training data comprising audience data and genre data for eachbroadcast network of the predetermined set of broadcast networks; and aprocessor training an encoder neural network based on the first andsecond training data, executing the trained neural network with inputdata for a broadcast during a first time segment on a first network fromthe predetermined set of broadcast networks, determining a similaritymeasure between the first time segment on the first network and a secondtime segment on a second network from the predetermined set of broadcastnetworks, and outputting the similarity measure between the first timesegment on the first network and the second time segment on the secondnetwork.
 11. The system of claim 10, wherein the trained neural networkis executed with input data comprising the first time segment of thefirst network and the second time segment of the second network and atleast one additional third time segment of a third network from thepredetermined set of broadcast networks, the trained neural networkdetermining similarity measures between the first, second and third timesegments.
 12. The system of claim 11, further comprising: a displaydisplaying a two-dimensional plot, wherein the processor further graphsthe time segments on the plot where time segments of the pairs ofbroadcast network identities determined to be similar to one another aremapped to the plot at short distances from each other and time segmentsof the pairs of broadcast network identities determined to be dissimilarto one another are mapped to the plot at long distances from each other.13. The system of claim 10, wherein the processor further determines atime segment of a network from the predetermined set of broadcastnetworks having a highest degree of similarity to the first time segmentof the first network.
 14. The system of claim 10, wherein the audiencedata includes audience demographic distribution data and the genre dataincludes genre distribution data.
 15. The system of claim 14, whereinthe second training data is extracted as n-dimensional vectors of apredetermined number of similarity descriptors for each broadcastnetwork of the predetermined set of broadcast networks.
 16. The systemof claim 15, wherein each broadcast network of the predetermined set ofbroadcast networks is characterized with 62 similarity descriptors. 17.The system of claim 10, wherein the first training data is generatedmanually or is generated based on a comparison of network programgenres.
 18. The system of claim 10, wherein the encoder neural networkis a Siamese neural network.